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IJAT Vol.16 No.3 pp. 261-268
doi: 10.20965/ijat.2022.p0261
(2022)

Paper:

Identification of a Practical Digital Twin for Simulation of Machine Tools

Tomoya Fujita*,†, Tiandong Xi**, Ryosuke Ikeda*, Sebastian Kehne**, Marcel Fey**, and Christian Brecher**

*Advanced Technology R&D Center, Mitsubishi Electric Corporation
8-1-1 Tsukaguchi-honmachi, Amagasaki, Hyogo 661-8661, Japan

Corresponding author

**Machine Tool Laboratory (WZL), RWTH Aachen University, Aachen, Germany

Received:
October 26, 2021
Accepted:
December 24, 2021
Published:
May 5, 2022
Keywords:
machine tools, feed drive, digital twin, simulation, identification
Abstract

A practical digital twin for machine tools is proposed in this study. The proposed digital twin is capable of time-domain simulation of machine tools and consists of a controller model, machining process model, and machine dynamic model. To predict the quality of the machined surface after the finishing processes, a precise dynamic model is required. The developed dynamic model consists of an interaction force model, vibration model, and friction force model. A linear auto regressive with exogenous inputs (ARX) model is adopted for the interaction and vibration models. Based on a systematic analysis of the disturbance forces of the machine tool, the friction characteristics are extracted to a displacement-dependent friction model and velocity-dependent friction model. A nonlinear Hammerstein model is adopted to identify the friction. Online identification systems based on the recursive least-squares (RLS) method are developed and tested for each model.

Cite this article as:
Tomoya Fujita, Tiandong Xi, Ryosuke Ikeda, Sebastian Kehne, Marcel Fey, and Christian Brecher, “Identification of a Practical Digital Twin for Simulation of Machine Tools,” Int. J. Automation Technol., Vol.16, No.3, pp. 261-268, 2022.
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Last updated on Jul. 04, 2022